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Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise.
Jinhao Song1, Jie Li1, Xiaokai Wei1
1National Key Laboratory for Electronic Measurement Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|August 26, 2022
Summary
This study introduces an improved adaptive Kalman filter to enhance navigation system accuracy in complex, changing noise environments. The new method boosts robustness and precision by adapting noise parameters and improving model selection.
Area of Science:
- Navigation Systems
- Signal Processing
- Control Theory
Background:
- Accurate noise parameters are crucial for optimal Kalman filter estimation.
- Variations in noise and measurement anomalies degrade filter performance, leading to divergence.
- Adaptive Kalman filters struggle in complex noise environments.
Purpose of the Study:
- To address estimation accuracy degradation and divergence in integrated navigation systems operating in complex, time-varying noise.
- To propose an improved multiple-model adaptive estimation (MMAE) algorithm for enhanced robustness and accuracy.
Main Methods:
- Combines Sage-Husa adaptive unscented Kalman filter with MMAE.
- Incorporates a forgetting factor as an adjustable parameter within MMAE.
- Improves hypothesis testing for better model competition management and parameter identification.
Main Results:
- The proposed method enhances system robustness against diverse noise statistical properties.
- Improved estimation accuracy is achieved in time-varying noise scenarios.
- The algorithm demonstrates superior performance in complex noise environments.
Conclusions:
- The improved MMAE algorithm effectively enhances the robustness and accuracy of integrated navigation systems.
- This approach provides a reliable solution for navigation in challenging, dynamic noise conditions.
- The adaptive parameter adjustment and enhanced model competition are key to improved performance.
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